Dataset for Climate-driven latitudinal divergence in lake phosphorus dynamics across Canada
Bibliographic record
Abstract
This dataset provides annual near-surface total phosphorus (TP) concentrations for Canadian lakes from 1984 to 2023, derived from Landsat surface reflectance imagery and TPNet model retrievals. It includes two data components: Grid_TP, containing the median lake surface TP for each of Canada's 3,329 grids (1° × 0.5°) with latitude (LAT) and longitude (LON) representing each grid's centroid; and Basin_TP, containing the median lake surface TP for each of Canada's 366 HydroBASINS Level-5 watersheds, with LAT and LON representing each watershed's centroid. Each watershed is uniquely identified by HYBAS_ID, which can be used to obtain its full boundary. BASIN_TYPE categorizes watersheds by land use: 1 for urban, 2 for agricultural, 3 for tundra, 4 for boreal forest, and 5 for temperate forest. TP concentrations are expressed in µg/L. The dataset also includes the pre-trained TPNet model (TPNet.pth), developed using the PyTorch framework, along with demo code (TPNet.py) to guide the estimation of near-surface TP from Landsat imagery. For any questions or suggestions regarding the dataset or model, please contact Hongwei Guo at guohw@chzu.edu.cn.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".